{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.导入工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"data/pima-indians-diabetes.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.数据预览分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 预览数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从结果看有8个维度的输入X，输出y=Target有0,1代表不发病和发病"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看数据的基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从结果上也可以看出数据总维度是9，都是数值型特征，特征工程时需要归一化处理。总共有768条样本。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看数值特征的基本统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出很多特征的最小值是0，有些特征的0是没有意义的，需要考虑进行均值填充或者抛弃"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 查看各个特征为0的样本点个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                       111\n",
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "Diabetes_pedigree_function        0\n",
      "Age                               0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "x_train = train.drop([\"Target\"], axis = 1)\n",
    "x_columns = x_train.columns\n",
    "print((x_train[x_columns.values] == 0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pregnants代表怀孕次数，出现0是有意义的\n",
    "\n",
    "其他的特征出现0是没有意义的，要在后面的特征工程中均值填充"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.查看特征的分布及其与输出的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （1）怀孕次数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 怀孕次数的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['pregnants'])\n",
    "plt.xlabel('Number of pregnants')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "怀孕次数主要集中在0、1、2等比较少的值附近"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 怀孕次数与是否发病的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12e6d3748>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PREDF = train.groupby(['pregnants', 'Target'])['pregnants'].count().unstack('Target').fillna(0)\n",
    "PREDF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面两个表可以大致判断，怀孕次数多时发病概率较高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （2）口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 血浆葡萄糖浓度的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.Plasma_glucose_concentration, kde = False)\n",
    "plt.xlabel('Plasma_glucose_concentration')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "除了在0位置附近的异常外，血浆葡萄糖浓度基本类似高斯分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 血浆葡萄糖浓度与是否发病的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "样本数太多，为了分组观察的方便，这里把Plasma_glucose_concentration按步长为10进行离散化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12f132c88>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个新的数组存储离散化的Plasma_glucose_concentration和Target\n",
    "glucose_group_list = [] \n",
    "\n",
    "# 遍历所有样本\n",
    "for index, row in train.iterrows():\n",
    "    # 按步长为10进行离散化\n",
    "    glucose_group_round = round(row['Plasma_glucose_concentration'] / 10)\n",
    "    # 组成一条记录添加进数组\n",
    "    glucose_group_list.append({'glucose_grouped':glucose_group_round,'Target':row['Target']})\n",
    "\n",
    "#构造一个DataFrame\n",
    "glucose_group_df = pd.DataFrame(glucose_group_list)\n",
    "    \n",
    "#查看\n",
    "gluDF = glucose_group_df.groupby(['glucose_grouped', 'Target'])['glucose_grouped'].count().unstack('Target').fillna(0)\n",
    "gluDF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图分布中可以看出血糖浓度越高发病的概率越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （3）舒张压"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 舒张压的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.blood_pressure, kde = False)\n",
    "plt.xlabel('blood_pressure')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "舒张压集中在60-80，0附近明显是异常值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 舒张压与是否发病的分布关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12f1235c0>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QcZDOQC1yEZHEKZGLiCROiVxEJHHqI8+ro/VJirQxHevoONQiFxFJnBK5iEjiunzXypCJ92/2/pWrjq2sXOZnJeinpUhVqMsyF7XIRUQSp0QuIpI4JXIRkcR1+T5ykc6gox2zyXtqYtn1KNd/Xqu+9by3Gmjjvn61yEVEEqdELiKSOCVyEZHEpdVH3sa3xsx7jnmtdLT6iHQUrTpGUCavlO3r70C36lWLXEQkcUrkIiKJ63BdK9nug8Kug3I/c8p2O+iy31zUlSO10C63xahBN0jZfJTzNMq826ZVLXIzG2tmi8zsRTOb2Jp5iYhIPrkTuZl1A/4D+D/ACGC8mY2oVsVERKQyrWmRjwZedPcl7v4RcBvwhepUS0REKmXunq+g2YnAWHc/K74/FRjj7ucXTHcOcE58uyuwKA5vC7xdZhHl4p091tHqo3XUOmod27c+O7p7XcnauXuuF3AicFPm/anAdS0oPytvvLPHOlp9tI5aR61jx6pP4as1XSvLgB0y7+vjOBERaUOtSeQzgZ3NbKiZ9QROBqZUp1oiIlKp3OeRu/t6MzsfeBjoBvzC3Z9rwSxuaEW8s8faY5lax+rH2mOZWsfqx9pjmc3VZzO5D3aKiEjHoEv0RUQSp0QuIpI4JXIRkcS12U2zzGw3wpWfg+KoZcAUd19YZNrt3H15W9Wto2nN+pvZAHdfWe065ZF3PdpjHVKqayo6y36chJacdJ73BfwDMBeYCHw1vibGcZcD/TOvAYSbhW0D1AHnAg8B8+LrQeDvgF2AXwA/APoANwLzgTuBYWXK9ShSvxcyw+cD28bh4cDjwGrgKcJ9ZUotc3fgKuB54B1gJbAwjtsB+BHwK+ArBcv+eZn170+4erZp2q3j9POA3wD/nqlrA7AEeBF4lXA17WPAr+PyHwHWEE4bPahMXfuV+Ryn5lyPE3OuwyFxXJ71yLvN89a13PZuoPT++IkY+2fggIJ6Xlzic3gh/t0jM64HcDHhFOAfAn9Px9mPy23TgXHeebbrXm2Rv1J4tVUif4HiCbQn4MDLBa+P49/3geuB/QgXHNXH4euB5cDXCF8I84Fvxw/6TOCtMuU+Bt6Nr/fia0P8+y7wXKZ+9wPHx+FD4w5UapkrCV9Yn8qU/1Qc91bcWcfFf7TfAp+I05Rb/yXA7Mz8bor/fDsS/lHXZGKPAfvG4V2ADwj/sOOBpU3/TMARhH/oUnX9E7B3kdc+wIc51+PPOddhFjAj53rk3eZ561pue79N6f1xCSGhXQg8Dfw4s4zZbNovi+2vGzLT/htwM+HL7xpgdQfaj8tt03uAZ3Nu1z/F4YFs2k8HtiJPHVdi/HDgBGBEfN89E+tD+PLpH98bMAb4YnyNIZ4dWGS+Xy94XyxHbtuREvnzhHsFFI7fkZCQHwJ2z4x/Of59ocw812WGXysVK1JuFfDf2Q+8aXlxeFFmeGZB2Q9zLnNdwft/BJ4gtFoaS61/HM4m8rmF823aqYAnq1RXB34f/6EKX3/Jsx6tWIdngTltuc3bYXtnk1x3wvnDvyO01OcQfgUU3V8Lts1cYiIgJJPs/0e77sfNbNO5hBZ/nu36PPBkLP9ofDWNOyn+XRq36TaZcjPYlGibXicAb8bhZ9n0C+FUQkP0pjj+14QvuxcIXzBLgGlxOT8k/JJ4ME5/U9wmLwL/CXwr8/o24Qv+W8DP4vZ7m/Crd0ix//9yr1yJuaUvYGxmBW+Ir6YVHEtoodwJ/BjoCyxp+lCBLwFbZOa1BfBlwjf1LoS7ML4NNMT48BgrVe4pQuvy98A34/glmemuJLRsdgK+T2gp7QicTmgB7gLsW2SZ7wHfZfN/uIGElswH2brE2ATgOcLPx6LrH6drzHzwS8h8uxOOM0wFDgcuA35CaJFdDqwAjorb4VVgXCxzCKFFVqqu7wM7l/gcP86zHq1Yh18RfiHkWY9c27xG2/t9Su+PHxbZzpcSEuTi+L7o/hrr15SEFhbM4y2a348L/3d2pjr78TUt2KbzgG/k3K5rCTfqK9x++8VtPhboB3wn1nFYjM8h7Mv3EbqYfhlf78W/qzLzmgkMiMNbEn6VbgsMJfxKGpbZRuvIJOHMPIYSfkXdDlwSP99LCY3KSwn71cg47YnAYmC/prp2mESe2XH3izvdCXG4W8E0xxGS95vx/ZC48ssJ34AvxOHbgVMId1JcCBxI+Jm3OMbPjtOsiGUWZ8oNzdTnm8AfgdcL6nE6IeG/HT/cBYRv2+PKLPMU4GpCi2AVoX9xYRz378CRJb7gFpda/8w/dfZVF8d/itBSOzSu1xxCi+EBQr9iA+Gq2weB3Qj/HKsIO/TYMnWdAOxa4jP8XZ71aMU69AD2zLkeubZ5BXU9rERd9ylSz9Wxnl+k9H58N5njIJl6nQV8XPD/s9n+Skg62UQ0MFPXafGzbOl+/JW4/RbG7VntbXpJ4TaNw037wOzMdj23xHZt+vyXlsk3fy54f1hcx/3iMvaN2+hrmWleziT6QXH4MaBXHO7G5r8QCvPGn8l0u2TG9yQcL7gzbsct4/imL7pnCqYfGT+fcVTYIu8QV3bGM1oGEXa6DYRvuflmNpbwoTnwEuGD3B9Y4O4PmNkYws/9mWY2kvBTZ4G7P5CZ94A4+BN3/2qRZW8PzHf3AYWxzDS/cvdTS8TuI/St/aVg/EGEFs+z7j61IHZgjM0vEjuI0NqYURirsGypZW6MEVqyz7v7GjPbktBXujfhn+Nhwp3X3jWz3sD3gL0ISeBh4OlYrlisqdyWhJbV3oR+32y5puWVKpetyw8Jn/nzBctsiq8EbnX3pUW20zeBuyuJxfk27XMVl2tBrCehj/d1QhIZCxwQ1+FmQsPmdXd/1My+AvwtIXneGGdxciZ+KqGl+q/AJEILblmRsr+MsaZyp8TYghj7UqbcKZn63AAMJnz57ED4f1wE/CZ+RjsVxF4oEetF6H+fFGPDYqy+sFzcRjvF7VBsmYVlFwO3EPrahxG+YJu2+w7A/yX0TQ9z943PUTOzPQhfWv3dfYCZbUH4NTCO8IvjNnffycwOJTw057eEA7d7E/bTA4FPE44P9CU8UGcOsYFDaMx9QHg2Q7Y+JwN3uPuPzOwLhF881wD/Ly5vFvA5d38zU9d6wi+GYe7el2a0eyKP/wDnEXa+UcAF7j45xl4HXiP0HT5CSETTgc8CHxEONBSL1RE+7KzDCT9PRxP6yIrFSqmk7EHuvk2s91lxne4h/Cwc7O47xNjZMXZ3idjXM+XuBb7o7qMz8z2/RNnmlpmd73BCi2O9md1A2Pl+SziA9G3Cjt4UWwvclSOWd57ZcnsS7mG/Z4n4xYRfXS8RDhje6e5vx3VeE6d9Cbg1xlaUiN2Ro1xLlncLYT/tTfgS3Sp+hkcABxN+vm9JaMH3ISSGI8LuRY9YtileadmDCAeLy5UrVp/PxHGPA8cQEtVq4HhCH/RuZWKfAf7QwnJfj5/zsTmW+fW4Dn91WjOhS2WJuz9JhpkNBv7J3c/OjBtESKwN7r5THLc14dfJLnH7NwKTCV/G5xEal9cRvpQnEPLUP8flFjvNekFmeVsRGjpj3P1gMzsSWOHuzxTUdWvgfHe/kuZU0myv5YvQQuzjm7pSZhGSOYT+qG6EnfFd4JNxfO8KYr8m/Fw7JP59Iw6/UCZ2CKHFVCq+uFwss04z2fTzcSs2/znWkljhwb5qzTd7IGx2wefRkWJzyfT9FomvJXQ5HEU4pW0F4djLacAzHSg2P9a3O6Hvult8b02fU4nYPGBezrK5Y5n3WwLT4/DgGsWauqhylW3v/NVRXu1fgczpfvF9n/gP8GNgbWb8nILpysXmEk5tegQYFcc19UdtUSrWXLyZ2DOEc2YHUHBT+Lgz5onNqdF8VwGnx+Ffsulg1y6EPtGOEptJ6FcsFf+gYL16EPpnbyXTv9wBYusJ/aTbEPqqm05V60U4QFYqtpDQPZGnbGtiTacUbkNm36lRbD4hkecpu4BwOmRTf372nPfBlLlWgnA+e6l4ubJl51smzz1YJlbu+oyfVZRHK5moli9Ct8SognHdCf1ezqYDA9kj/lsTfsaWis2Ow01H0a/jr09dKhnLU5ZwMGMJm87/3j6O70PoBsoTm1uj+c4j9M2+RDgu8XGc5g+EvtKOEtszfp6l4guL7VNxPed2oNh3Y51fJRywnEbo/36WcBCvVOxSQsMhT9m8sQfi/nEjIVk1fYnWEc4yq3bsceCCnGXfofg57xMJB3hLnQ8/ldDnXSpermyp2ETKX4Oxokys3PUZaRzsjJ366z3T0Z+JHeru04uM35bQBzy7RGx7d382M+5YwlVz3y8yfclYa8vGabYknE3wcrVi1ZqvmX2ScGpUd6DR3d/KTNdhYqXiZraLu79QYvt0mFiMfxrA3V83s36Eg2OvufuMcrHWlG1FbCShv3u+uz9fsB5Vj+Uta2aL3H3XEtv7I3fvWSK2KK57nrLlYk44y8WKhA8tEzvE3Tfe98rM/pFwPOA44BF337vY8jZbdnsnchGRPMxsKuFg6KSmL34zG0g4+Phdwql+xWKfBf6Ss2y52D8RbhtQeKIFZvYx4crQUrFPeObMNzObAFxEOH64Y3PbQnc/FJFUfZlwDOgPZvaOmb1DOHOtP6HLolTsS60oWy52HqVz6o/LxO4lnP22kbvfTDij66NKNoRa5CLS6ZjZ6e7+y5bGWlO2rWObTadELiKdjZm95u6DWxprTdm2jmW12f3IRUSqyczmlQoB9SXiBgxsRdm2jg0sUc/NJ1SLXERSZGZvAUcTrovYLEQ4XXWfErH/JVxMmKdsW8f+190/TTPUIheRVN1HOKtjbmHAzF4pE5tOOHc7T9m2jk0vHFeMWuQiIonT6YciIolTIhcRSZwSuYhI4pTIpc2Z2RAzm19k/HQza6jC/CeY2XWtnY9IKpTIRYowszY7o6stlyWdkxK5tJfuZnaLmS00s7viXRk3MrPxZvasmc03s6srGH+6mb1gZjMIt8YtycxuNrP/NLNZsczn4vgJZjbFzH5PuLUrZnaRmc00s3lmdnkct5WZ3W9mz8R6fDmOv8rMFsRp/zWzrBMzy34//j3UzP5oZlMI99XGzL5qZjPMbK6Z/ZeZdWvF9pUuRC0BaS+7Ame6+xNm9gvCY7uAjbdtvZpNF0lMNbNxhMfsFRv/FOE5lvsQHlP2GOGhHOUMITy6bxjwmJkNj+P3BvZw93fM7CjCk+VHEy7OmGJmBxPuhf26ux8b67u1hWfDHg/s5u5u4dawzdkb+Jt4S+HPEG7kdIC7f2xmPyM8A/K/K5iPdHFK5NJelrr7E3H414QHHDTZl/BIr+wzLw8mPGik2HgKxt9OeIJQOXfE24YuNrMlhOdCQrj/8ztx+Kj4avpS6ENI7H8E/i3+IrjP3f8Yu0fWAT+38EDu+yrYBjMy94w/gvBFNNPMIDyycHkF8xBRIpd2U3glWltfmVZq+R9kxhnwI3f/r8LCZrY34eb/PzCzae5+hZmNJiTkEwkPyT6c8Ji3LWKZLQiPWGtSuKxJ7v69/KskXZX6yKW9DDaz/ePwV4D/ycRmAIeY2baxn3g84dFupcY/FccPMLMehPtNN+dLZraFmQ0DdgIWFZnmYeAMM+sDYGaDzGy72PWz1t1/DfwLsHecZmt3f4DweLY94zxeIbS0ITzxpUeJ+kwDTjSz7eKy+ptZsw8UEAG1yKX9LALOi/3jC4Drgc8DuPsbZjaRTY/Gut/dJwOUGX8Z4ZmJqwnPOm3Oa4Qvhk8Cf+fu62KXxkbuPjX2Xf8pxt4HvgoMB/7FzP5CeIbo14C+wGQz6xXr9q04mxvj+GcIDxX/gCLcfYGZXUzo998izvc8wnM1RcrSvVakyzGzmwl923e1d11EqkFdKyIiiVPXinRaFp5GXthffqe7T2iH6ojUjLpWREQSp64VEZHEKZGLiCROiVxEJHFK5CIiifv/x89OeVoYYIgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bldDF = train.groupby(['blood_pressure', 'Target'])['blood_pressure'].count().unstack('Target').fillna(0)\n",
    "bldDF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里看不出太明显关系，交给后面的训练模型来分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （4）三头肌皮褶厚度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 三头肌皮褶厚度的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.Triceps_skin_fold_thickness, kde = False)\n",
    "plt.xlabel('Triceps_skin_fold_thickness')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0样本数较多，需要处理，而且有长尾效应"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 三头肌皮褶厚度与发病率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12f8c6a58>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "triDF = train.groupby(['Triceps_skin_fold_thickness', 'Target'])['Triceps_skin_fold_thickness'].count().unstack('Target').fillna(0)\n",
    "triDF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0样本太多，去掉0样本再观察"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12fad3748>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KToFcRKTgFMhFRApOgVxEpOAUyEVECk6BXESk4BTIRUQKbuf9zc4d1A77MLCuIOcHktVzW3WJ/aILPNBtR6UeuYhIwSmQi4gUnAK5iEjBKZCLiBScArmISMEpkIuIFNyOPfwwOdwJNORJOkSH/I6mhupJFdQjFxEpOAVyEZGCUyAXESk4BXIRkYJTIBcRKTgFchGRgtshhh+OmPKbbe9fvPLo1um1DgurYehXsg6l9SiyLvHUvAIrdPvVMgRSQ347hXrkIiIFp0AuIlJw7Tq1YmYvAuuBrcAWd2/Ko1IiIlK5PM6Rf8bdX80hHxERqYFOrYiIFFx7e+QO3GdmDvzQ3aeVzmBmk4HJAMOHD6+5oKyRKSL1siP+xmY9R3ZJx2lvj/xgd98f+BxwlpkdWjqDu09z9yZ3b2psbGxncSIiUqpdgdzdl8e/q4BfAePyqJSIiFSu5kBuZu83s/4t74G/ARblVTEREalMe86R7wb8ysxa8vmZu9+bS61ERKRiNQdyd38B2DfHuoiISA00/FBEpOB2iIdm5a3QDzoSyUldj4Os4Yx6CFdF1CMXESk4BXIRkYJTIBcRKTgFchGRglMgFxEpOAVyEZGC0/BDEclNuacp5j6csczQxJ3taanqkYuIFJwCuYhIwSmQi4gUnAK5iEjBKZCLiBRc54xayXhATvJKM+wcV5tFiijvEShZ+dX8m6K11KFM/Onqo2DUIxcRKTgFchGRglMgFxEpOAVyEZGCUyAXESk4BXIRkYLrlOGH+k1MEek0Wb8PmnN+ZYdTl6tDDfVTj1xEpOAUyEVECk6BXESk4NoVyM1svJk9a2bPmdmUvColIiKVqzmQm1l34HvA54AxwAlmNiaviomISGXa0yMfBzzn7i+4+zvADOAL+VRLREQqZe5e24JmxwHj3f30+PlE4EB3/2rJfJOByfHjaODZ+H4X4NWM7GtJ6+r51bOsrp5fPcva2fKrZ1ldPb96ltXR+e3h7o0ZeYC71/QCjgOuT3w+Ebi2iuXn5ZnW1fMrct3VFsXJr8h1V1tUtkzaqz2nVpYDuyc+D4vTRESkjtoTyOcCHzazkWbWCzgeuCufaomISKVqvkXf3beY2VeB3wLdgR+7+zNVZDEt57Sunl89y+rq+dWzrJ0tv3qW1dXzq2dZ9czvPWq+2CkiIl2D7uwUESk4BXIRkYJTIBcRKbguFcjNbLCZDe7seoiIFEldL3aa2W7A0Phxubu/YmbDgW8DRwBrAQM+ADwETHH3F+tWwS7MzAYA40m0H/Bbd19rZh8hPB4hmXaXuy/JSgP+Uia/vMvyMstUXRbwR8IjIpLT57i7t5Ffahqwrkx+lnNZXmaZqstqo+551yE1jXC81rIvlUvLu6y8j5Fy+dXS7pn5UYG6BHIzGwv8ABhA601DwwiBuy9wBXC7u2+N83cH/g44FziK6g/0WgNH1gb4YJn86rGDQHjMwX0l7fdZYB7h0QczgOZE2vHACmBIStpXgH7Az1PyewA4Mseyvhbf/2fKMs8CTVWWdQZhP5pbsswo4A7gixn5Za3XRMLw2fkp+V0PnA4szamsY+P7O1KW+SXwt1WWVa7uWcvUWoesttgP2ArMrKId2krLqketZeV9jJTLL2ufLtfu5fKb4e5X0oZ6BfKngDPd/Q8l0w8C/svd35ex3ErgTarbOLUGjqwd4e+ADcB1Kfll7QR57yBTgSvdfWpJ+wwCXgHe7+6bS9J6xXqnpT0L9HD3PTPy29Xd1+ZU1p8I+9mHM5aptqwlQG93H1kyfSShp75bmfzSynoW6OnuH8rIb3Tpt8J2lLU0tsWolGVW1lBWW3VPW6Y9dUhLewHY7O6jq2iHttKy6tGesvI+RsrlV+22L5ffM6XHTpp6/Wbn+0uDOIC7P25m75jZ94HpwLKYtDswidBb/0iVG+dUQoNdWTL9KtoOHGlpRwLvlskvrQ7l0k4Btrj7V6pcpxMJAT3p3fj3r4CXStKGEL6ZpKX1JPRsSrXkl/bfvdayuhFOl5UaUmNZvYF3UpZZHsspl19aWg/S26Ilv+YyadWWZaS3xbs1ltVW3fOuQ1raFsL+lJYfGXVoK60jysr7GMnKL6se5dq9XH7vUoF6BfJZZvYb4Ga2D9YnAT8BngYupfV0QjPwa0Jgq3bjtCdwZKWl7TzldoK8d5AfAFea2XW0tt9wQi/+u8CD8T9+Mm0UcFlG2vuBrRn5TQfmm9l9OZXVF8DMZqUs8581lPUB4HUzu5Dt96XjCV+5s/LLWq+BwIaM/B4C5prZjJzK6h+aIrXdZ9ZQVrm6Zy1Tax2y2uJ9QL8a9qVyaVn1qLWsvI+Rcvll7dPl2r1cfts9TTZL3S52mtnnSD+XfE+ZZSYBFxNOd5Su/J0xv9KV/3h8v4D3NsqdhHNVafk9BByekpY8D1ma343AKSl1KJeWPM9X6TqNAqYQ/vGWnld/3cy68d4LQ3PdfWtWGiEgHpWR36AyabWU5WWWqbosYC/S96XFbeSXmkb2NZDFZvbRnMuizDJVl9VG3fOuQ2oa4TRiLftSubS8y8r7GCmXXy3tnpkfFejSt+ib2f8CHqP6A73WwJG1AdJGBpTdCTpiB6mu9URkp+FVPPO2I17A5DJpl3Z2/br6C5hWJu3uatPayC/vssotU3VZwNQa80tNayO/vMsqt0zVZdW4TK11SE1rx75UdT3qvN/Wml8t7Z6Z33bzVTJTR76AfyKcKz8yfv4H4FrgLMIV+dw2TjsCR9YGqHonyHsHAQ4os8yQatPayC/vssotU3VZwOdrzC81rY388i6r3DJVl1XjMrXWITWtHftS1fWo835ba361tHtmfslXvYYffg34lbsvS0m7hXDuty9hXHk/wljLIwDc/eSMPA9w9ycy0oa4+4pKp1eQX2paG/lVnVbLOtWDme3q7qtqWK7B3dd0RJ1E6q3ccdDp+3ol0b69L8I55r8AjxB64I2JtIXxbw/CKJXu8bO1pHXWizAcsdplGjqgHgOAKwljeV8D1gBL4rSBZZa7D/h/hJFB/5CY/kFgEfA9oIEwrPFpwtj2jwKDE68G4EVgEHBcSZ1uABYCPyNcrd8lpjUBLwDPAW8TbibZM6V+TcDvgJ8SRiXcH/eVucBfE0YUPBOnrQYeB04FzgTujWUvBGYBX6b8N7gfxeX+FfhUYnrfmNcFhKGNJxMuqn0b6JeSz5/i348npvUELorL/RtwXqItRgGzCZ2U1wgXrdPy/RDwY+ByQmfmR3Eb/QIYGdf7N4SL+PMJ9xocUW1bEC7cP1LaDjHtUuCfq2mLGtvhD4T7Nv53DW1xQUo7HEb+x8hgwv6ddhxcQ/q+/hIwIaMe3435pR1ze5K+r59ccYzoyECYaJQnCcMC/yauzOq4800CFgO9YgOtBwbHZXoTbuCpauMQLiQ+X7phYtqNhBt7qglgxyXqVGnweomwk19ESQCjtuD1NHAh8MGSYHwh8Htg/5TXAcDG2FbHEA6uXxKGcN1LGBUzJa7LhbEuZxMuFP+55LU5/n07Uf71hANtD8IBuy6R9jvgE/H9MsINHi8TbrE+D/irmDYH+BxwQpzvuDj9iLi9TybcEPV14F+AD8c2fgI4KKYNi++vA35Vsg2T2/LNuM3OjctfFcv6OaED8X3gQcJpvUOA/x/X+434Wh9fW1v+Jtb334GbgE8DVwNrE2m/AY6N71cTflD3tVjusUCvmDabcLPYFELQ+kbcJqcROkFTgYMJQeQywiinlYSgXE1b/AR4q7QdYh1ej+tSTVtsBd6osh0OI/yDv73KtniUsB+WtsMDhLiQ5zHybqxj2nHwTsa+vhfhuE2rx7OxjmnH3ArS9/XpwL91pUA+v+RzT8KwvlsJN868QAh+X4s70I8Iweu5GjbOgzHP7TZMXHZdbLhqAtjbwAtVBq+94nLfoSSAUVvwWpe1QWO9H4rll77eLZn3/xJGAS1s2SbAyyXzLCcE+n0S0/5cuh2Bp0qW20S4Ew7g8eS2B56O7w8hBImVsX4vJ+YrrcfGks9z498/AX8s0xYvlGzDls/vJubrQfgFljsIPbsnCd8AV9I6kssIQfdmwh2SpW3xZLItiD3guNym0nq3LBPb/gOEG7zuIQT3G4HnqmiLx+PfpcCSKtviHWIgKmmH9wFvJdahoraopR2S61RNW8S2ezKlHd5HopOR0zHyL4TjLu04WELKvh4/p9Yjbvtnq9zXu5Gxr78n/0pmau8r2fgpaX0JAa6llzaQ0Ase17LiVW6c9cmGSWyYhpYdNaMxUwMYNQSv0o3D9gFsPXGkThUb9D5gVclBtBvhH9EG4MMZbbQZ6FYy7eRY75fi58tL0p8m/CP5BXAV4UaGln9kzYR/MN8gBAUrab/7CGPxpwL/QeiZrQB+UlJGd8KzZlYRvqX9HeEf+TEx/dNxvQ6OnycShmdC+IayPLlecYf/+7hew7PaImXaJbGcpfHzj0vSFxB6bQ8ROhndEm3xAuF5IF+kJJgSevg3EU4RfJPQ+92D8A3v7pJ5GwinQt4gdADGEYJmU0wfRehB7xk/7w/MTrTFsmragvDtdllKOzzG9t+4KmqL+Dq2ynY4hUQnqExbfKKkLRYRA1uyHeLnDYTTQnkdI88Q9ve04+Bs0vf1SwnfntLq8RfggYxj7k1a9/UvEPf1+DkzBm6XRyUztfcF7FXjcvdVu3EI/y1Ld9SWDZP8SlRRAKO24HUpsCalbt1j/X5LdcFrEOG00h8JX39fi/l8K67b6Iz2u4M4Gqhk+i0kej2J6aMIDy8jUYfHgZWJAz75aozTP0jorR0G3EbofTxN6GXNJft87b6xLWYBH4nttzZuq5MI315eJ3ylHp04gOcT/gn8Kb5WxXIvBvbNKGsuMD5l+iOkB/k9gUfj+26E4PUI8Jc47caS126Jtngwbpc/EALResIpxJeBARn1O4LWr98HE75JLo3rdlFcdimhc3Fgoi2WEHqyf0rMn9kWhFN630+ZfjrhdELaOevMtiAE6nLtcEpKO/wb8FiZ4z6rLV6P6/pcbIeD4vyNcd/5FvkdI+Np/Qe/3XEQpx3Ge/f1ybEuafV4DNg945i7n+339b0S6/W1imJlLQG2Xi9CAGtplNdKNs6ktI1DuDBzccaGWZOxo2YGMGoLXpOB2zLWqVzwOrHMBr2c8FCvfinr9ZG481eTdkYlywB9gI+1s6zTyyzz0TbS0tb5HELPtQH4FHA+MCGmjaP1FNcYwj/hsmkZ048mnB5Iph1CCJAt+R1YYVl7EzoCWWWl5bd3yXp9Mmu5OK0hvn5a5ni6uZrpyTQSHZj4eQgpnZUK8vtJtfWLaXfT+viNXSpYp0Nim/9NStrBsf0qTov5XZSxzLay4jYcEKf3JZzHv5sQK4bF6X0Inb1fE2LZAMI/ywsI192uInwz+UBWe5S+uvSdneWY2SnufmM1adUsY2Z9CF9nF+WRX3vqF4dv/h9Cz2YscI67z4xpywhfvZdUmmZmZxMuYN2bR37tLOtNwj/qitLM7BLCt7QlhJ7MOOBhwkWvdwj/fHrEtAMJp9vKpZ1GCBCrKlimXFmVpOVd1rkxr6W0Opxw+mMcoVPQwoDPEL71kUhrmV5umbS0lrJK8ytXh0rqV2tZh7j7IAAzO51wH8qdhOA63N13j2lnxLRftZF2PnCFu18Zp/9TRn6lZY0Chrr7FjObRtiHf0no+T/m7sfG6W8RLvYeQThF9wrhIu8EQqdwLeGU1T+5+8O0pdKI39VelJxfriStlmW6Qn6Env6y+H4E4XG358TPG4k91krTYn4L8sqvnmXF/J4k9HbeIPZaCL2cjYTTV9WkLSJcRMsrv3qW9STh29thhFNzhxGuSXyaENx/mpK2lHA6sJplPk04dVNtflnLtFVWTXVPHC9zaf3m/H62v15VadoCWi/SV5Nf8gJv8vraEuI1Nt47+GMjrcOu+wIPx/fDKXN9cbs88gqsHfGidWxs6etpwvm8tLSNGWnllunq+W1i+wtR/Qg93Kt47wXSStJeJXHhNof86lnWKloPiCdL5klezK4ojRAMc8uvnmURevfLCb30sXHaC4m080rTsqaXW6ae+bWjrAWEU7ENwLyS9ttYbVrMb2EN+b0OnBLf38QBr8EAAAZWSURBVEjrhdpZtI56SU7fi9A7bxlZN6ikHosqipWVzNRZL8LXjbGEK93J1wjC+NW0tNWEg72aZbp6fv8NrCppmx6E827eslNXkbaCxDjoHPKrZ1mrW/Jj+9EaAwhfY/tWmTaPGBxzyq/eZc2n9SL9tbx3JFRqWi3L1DO/assijAhqGW75AvHWdkIH4J0a0l6idRx5NfktJFwAfp5wKnRznOdRwhNPS6f/F+E8+ULCsOs/0vqPoJHEyJyysbLewbmaF+Hmm4Mz0p5PS4vL3F/NMl09v7jj3pGxzDEkxtlXkhbzy3pmRdX51bMswpjhT6VM3wXYPyOvcml/RWLIaQ751bus5HDZo8m+3yA1rZZl6plfrWUl5ukLjMwrrdJlCOPj9yUM2UyOusuavjdh2PVHyq1P1quwFztFRCTo1tkVEBGR9lEgFxEpOAVyEZGCUyDfCZlZg5k9FV8rzWx54nOvknl/a2b9O6uupczsUTMbmzK9pnqa2RgzW2BmT5rZiIx5epjZ2oy0n5rZMWXy/7qZ9a4gn7PM7B/L5HOkmd1Zbl1k59Wjsysg9efhAfhjAcxsKrDB3b+TnMfMjHBb9lH1r2H12lHPvwVudfcr86xPwtcJz9beVG4md/9eB5UvOwH1yGUbMxtlZovjrzY9Awwxs2YzGxjTTzGzhbEHe2OctpuZ3WFm88xsjpkdFKdfbmbTzexxM1tqZqfG6UNjr/opM1tkZn+dUZceZvYTM3s6zve1kvTusTc8NX5uNrOBcR0WmdkNZvaMmc1q6RGnlDER+Cpwtpk9EKf9c1x+UXy8QOky3czs+2b2RzO7nzAEMKs9zwN2BR5pyT9OvzK24e/NbNdEe50b3+9lZg/FeeaXflMwswPj9JFxuRvM7L/M7AUzOysx36S4TZ6Kde6W1a5mdl7c9gvN7KdZ6yRdVC1jFvXacV6EpzaeH9+PItxd2pRIbyY8Wnhfws0KLT+y0fL3NlqfQjeCeCca4SFf8wk/ELJrzKflyZUXxnm6k/IQs5h2IDAr8Xlg/Pso4fGmP2/Jp6Seowg3W+wTp98BHF9m/S8Hzk2UuYBwW3x/wm3V+xC+ua6N83yJcJdeN8IY+TeIT7DMyL85UfcehBudPhc/XwVMSanHE8Sx97H9+hIeHnYn4QFN82h9ANPlhCcR9ortvCa268fi/C2PWZ5G+D3crHZdQesPO2T+oo5eXfOlUytS6nl3n5cy/XDCEx1fA2j5Swgwo8OZGAAGWXjgGMCd7r4J2GRmswkBeC7ww9hLvtPdF2TU47mY738Sfl3mvkTa9cDP3P1bWcu6+9Px/ROEfzCVOBj4pbtvBIjnpA8hBPQWhxJOxbwLNJvZwxXm3WKju89K1O2QZKKZDSI83e/XALH9iO37McJz7T/r7isTi93t7u8Aq8zsNcIdgUcS2nteXLYP4dnlvyW9XZ8BfmpmMwn/AKRAdGpFSr1Z5fwGjHP3sfE1tCUQEnqfSe7uD9H6sKObsy7weTiP/3FCb/Ms4IeJ5P8GjjCz92XU6e3E+610rWtB7yTeV1u3vxC+bexbMj1tfY3w4xAt22W0u/9rmXY9CvgBIfjPMbPuVdRLOpkCuVTqIeDvzWwwQMtfwu8lJs/LJkeUHGNm7zOzRuIpATPbg/CA/mmEhwftl1ZYXMbc/ReEZ4Dvn0j+YSx3hpnlGaQfAY41sz5m1o/way2PlMwzm9AO3cxsKOHJe+WsJ5ymqYi7vw6sNrPPA5hZbzPrG5NfI9yW/h0zOyQrj+gB4EtmtkvMp8HMhqe1awzaw+I/2X8mnPfvm5WxdD1dqaciXZi7LzCzbwOzzWwL4bTAaYQgfp2ZnULYn35Ha2BfRHgoUANwibu/Ei96ft3MNhOC3IkZRe4O3GDhvIATzq0n6/NtM7sCuMnMTsppHeeY2a2E0z8A17n70yX/LG4nPDe75Rd/ft9GttOAByw8X318hVX5R8LppysIPfgvJuq4Igb5e8qtd6z3pbHsboSe/JcJPfbSdu0B/MzC8M1uwHfcfX2FdZUuQM9akQ5hZpcDr7r7NZ1dF5EdnU6tiIgUnHrk0unMbB7vPc33D+6+OMcyfgAcVDL5Kne/Oaf87yL8okvS+e7+QNr8InlSIBcRKTidWhERKTgFchGRglMgFxEpOAVyEZGC+x9/SgtvhCxBBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tri_list = [] \n",
    "\n",
    "# 遍历所有样本\n",
    "for index, row in train.iterrows():\n",
    "    tri = row['Triceps_skin_fold_thickness']\n",
    "    # 过滤掉0样本\n",
    "    if (tri > 0):\n",
    "        # 组成一条记录添加进数组\n",
    "        tri_list.append({'Triceps_skin_fold_thickness':tri,'Target':row['Target']})\n",
    "\n",
    "#构造一个DataFrame\n",
    "tri_df = pd.DataFrame(tri_list)\n",
    "    \n",
    "#查看\n",
    "triDF = tri_df.groupby(['Triceps_skin_fold_thickness', 'Target'])['Triceps_skin_fold_thickness'].count().unstack('Target').fillna(0)\n",
    "triDF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "总体上发现三头肌皮褶厚度较低时发病率较低"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （5）血清胰岛素"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 血清胰岛素分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.Triceps_skin_fold_thickness, kde = False)\n",
    "plt.xlabel('serum_insulin')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0样本数较多，需要处理，而且有长尾效应"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 血清胰岛素与发病率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "与上面处理一样，先去掉0样本再观察,此外数据的最大值是846，比较大用20的步长离散化一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1308df9e8>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ser_list = [] \n",
    "\n",
    "# 遍历所有样本\n",
    "for index, row in train.iterrows():\n",
    "    ser = row['serum_insulin']\n",
    "    # 过滤掉0样本\n",
    "    if (ser > 0):\n",
    "        ser_group_round = round(ser / 20)\n",
    "        # 组成一条记录添加进数组\n",
    "        ser_list.append({'serum_insulin_group':ser_group_round,'Target':row['Target']})\n",
    "\n",
    "#构造一个DataFrame\n",
    "ser_df = pd.DataFrame(ser_list)\n",
    "    \n",
    "#查看\n",
    "serDF = ser_df.groupby(['serum_insulin_group', 'Target'])['serum_insulin_group'].count().unstack('Target').fillna(0)\n",
    "serDF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也可以看到在数值比较小时发病率较低，数值大时发病率较高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （6）体重指数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 体重指数分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.BMI, kde = False)\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "集中在25-40之间，有部分0样本点"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 体重指数与发病关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "与上面处理一样，先去掉0样本再观察,用2的步长离散化一下数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x131671cf8>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bim_list = [] \n",
    "\n",
    "# 遍历所有样本\n",
    "for index, row in train.iterrows():\n",
    "    bim = row['BMI']\n",
    "    # 过滤掉0样本\n",
    "    if (bim > 0):\n",
    "        bim_group_round = round(bim / 2)\n",
    "        # 组成一条记录添加进数组\n",
    "        bim_list.append({'BMI_group':bim_group_round,'Target':row['Target']})\n",
    "\n",
    "#构造一个DataFrame\n",
    "bim_df = pd.DataFrame(bim_list)\n",
    "    \n",
    "#查看\n",
    "bimDF = bim_df.groupby(['BMI_group', 'Target'])['BMI_group'].count().unstack('Target').fillna(0)\n",
    "bimDF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看得出来胖子发生糖尿病概率更高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （7）家系作用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 家系作用分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.Diabetes_pedigree_function, kde = False)\n",
    "plt.xlabel('Diabetes_pedigree_function')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "值比较小，小的地方分布较多，有长尾效应"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 家系作用与发病关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "与上面一样，先以0.1的步长离散一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x131cb15c0>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dp_list = [] \n",
    "\n",
    "# 遍历所有样本\n",
    "for index, row in train.iterrows():\n",
    "    dp = row['Diabetes_pedigree_function']\n",
    "    dp_group_round = round(dp * 100 / 10) / 10\n",
    "    # 组成一条记录添加进数组\n",
    "    dp_list.append({'Diabetes_pedigree_function_group':dp_group_round,'Target':row['Target']})\n",
    "\n",
    "#构造一个DataFrame\n",
    "dp_df = pd.DataFrame(dp_list)\n",
    "    \n",
    "#查看\n",
    "dp_df = dp_df.groupby(['Diabetes_pedigree_function_group', 'Target'])['Diabetes_pedigree_function_group'].count().unstack('Target').fillna(0)\n",
    "dp_df[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "家系作用比较大时发病率较高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （3）年龄"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 年龄分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.Age, kde = False)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "样本点主要是年轻人"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 年龄与发病的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x131f146d8>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ADF = train.groupby(['Age', 'Target'])['Age'].count().unstack('Target').fillna(0)\n",
    "ADF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "人到中年明显发病率较高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### （9）发病的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Target'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大部分人是比较健康的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.特征的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1326390b8>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大部分特征相关性较弱，年龄与怀孕次数有较明显的相关性"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
